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Skillset, topics, projects and virtual internships for DS

This post is for those who are beginner and do not have any idea about topics that they need as a beginner DATA SCIENCE/ DATA ANALYST.  I am also facing the same problem before a year ago and till date I have some relevant knowledge about data science and also have some projects.  People are saying the we need so many skills like Mathematics, Programming language, some cloud concepts too. Actually they are right. Being a Data Scientist is not like being a web developer or a front-end developer that have limited skill set.  In this post I will tell you the exact topics that you need to learn at beginner level. MATHEMATICS Descriptive Statistics, distributions, hypothesis testing and regression analysis. Bayesian Thinking, conditional probability, priors, maximum likely hood. Vectors and matrices Matrices operations Eigenvalues and eigenvectors Linear and non linear functions Multivariable calculus  PROGRAMMING LANGUAGE(Python or R)    Data types, String oper...
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Univariate Analysis

Analysis of Data is one of the most important activity for any organization. Big techie's like Amazon, Google are doing their daily activities on the basis of data.

There ard two reasons for analysis of data.
1. By analyzing data, we have an idea or stats of anything related to any prospectives.
2. Some times these analysis are used for taking future decisions.(For predicting outcomes)

Univariate analysis is the very first analysis technique that should be done in every analysis scenario.

What is Univariate Analysis ?

 It is the simplest form of analyzing data. 'Uni' means one , so this is analysis by taking only one variable or we can say when data has only one variable, it is called univariate analysis.

It's main purpose is to give description about data.

How it's work ?

 Step.1  It takes data.
Step.2 summarize data.
Step.3 Find different different patterns in the data.

Patterns like min value,max value, average, IQR etc.

Univariate variable

 Variable can be any condition or categorical representation like age, height, number of goods etc that given into your data.

There are so many options by which you can describe your data like  Barchart, Histogram, Frequency polygon, Pie charts etc.

example-
This is Visualization of systolic blood pressure of persons with frequencies.
From the above histograms, we can get the following stats .
Histogram is bellshaped (unimodal), mean or center is about 115 approx. Range is approx 169 (max -min)
And it has some outliers as distribution is going till 244 on x axis.

 If you want to know more about Univariate Analysis,  or have any doubts just drop a comment or connect with us from the following link.

 - www.instagram.com/kavyansh.pandey

Thank you for reading this post.

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